All experiments with SubTB(\(\lambda \) ) are based upon the published code of [1]}, which extends that of [2]}. The proxy model giving the reward, the held-out set of molecules used to compute the correlation metric, and the GFlowNet model architecture – a graph neural network – are identical to those in [2]}, and the off-policy exploration rate and early stopping likelihood are the same as those tuned for the training with the TB objective in [1]}. All models are trained for a maximum of 50000 batches of 4 trajectories each. (Some training runs terminated early because of numerical overflows in the gradients, in which case we report the metric of the last stable model whose cumulative number of batches is a multiple of 5000.)
